As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.

This paper presents a real-time and memory-efficient arrhythmia detection system with binary classification that uses antidictionary coding for the analysis and classification of electrocardiograms (ECGs). The measured ECG signals are encoded using a lossless antidictionary encoder, and the system subsequently uses the compression rate to distinguish between normal beats and arrhythmia. An automated training data procedure is used to construct the automatons, which are probabilistic models used to compress the ECG signals, and to determine the threshold value for detecting the arrhythmia. Real-time computer simulations with samples from the MIT-BIH arrhythmia database show that the averages of sensitivity and specificity of the proposed system are 97.8% and 96.4% for premature ventricular contraction detection, respectively. The automatons are constructed using training data and comprise only 11 kilobytes on average. The low complexity and low memory requirements make the system particularly suitable for implementation in portable ECG monitors.

We show teachability of a subclass of simple deterministic languages. The subclass we define is called stack uniform simple deterministic languages. Teachability is derived by showing the query learning algorithm for this language class. Our learning algorithm uses membership, equivalence and superset queries. Then, it terminates in polynomial time. It is already known that simple deterministic languages are polynomial time query learnable by context-free grammars. In contrast, our algorithm guesses a hypothesis by a stack uniform simple deterministic grammar, thus our result is strict teachability of the subclass of simple deterministic languages. In addition, we discuss parameters of the polynomial for teachability. The “thickness” is an important parameter for parsing and it should be one of parameters to evaluate the time complexity.

Display devices play image files, of which contrast enhancement methods are usually employed to bring out visual details to achieve better visual quality. However, applied to high resolution images, the contrast enhancement method entails high computation costs mostly due to histogram computations. Therefore, this letter proposes a parallel histogram calculation algorithm using the column histograms and difference histograms to reduce histogram computations. Experimental results show that the proposed algorithm is effective for histogram-based image contrast enhancement.

Due to network users' different time-preference, network traffic load usually significantly differs at different time. In traffic peak time, network congestion may happen, which make the quality of service for network users deteriorate. There are essentially two ways to improve the quality of services in this case: (1) Network service providers (NSPs) over-provision network capacity by investment; (2) NSPs use time-dependent pricing (TDP) to reduce the traffic at traffic peak time. However, over-provisioning network capacity can be costly. Therefore, some researchers have proposed TDP to control congestion as well as improve the revenue of NSP. But to the best of our knowledge, all of the literature related time-dependent pricing scheme only consider the monopoly NSP case. In this paper, a duopoly NSP case is studied. The NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSP by considering their own preference, congestion status in the networks and the price set by the NSPs. Analytical and experimental results show that the TDP benefits the NSPs, but the revenue improvement is limited due to the competition effect.

We seek a resource allocation algorithm through carrier allocation and modulation mode selection for improving the quality of service (QoS) that can adapt to various screen sizes and dynamic channel variations. In terms of visual quality, the expected visual entropy (EVE) is defined to quantify the visual information of being contained in each layer of the scalable video coding (SVC). Fairness optimization is conducted to maximize the EVE using an objective function for given constraints of radio resources. To conduct the fairness optimization, we propose a novel approximation algorithm for resource allocation for the maximal EVE. Simulations confirm that the QoS in terms of the EVE or peak signal to noise ratio (PSNR) is significantly improved by using the novel algorithm.

The objective of this research is to design a high-performance NAND flash memory system with a data buffer. The proposed buffer system in the NAND flash memory consists of two parts, i.e., a fully associative temporal buffer for temporal locality and a fully associative spatial buffer for spatial locality. We propose a new operating mechanism for reducing overhead of flash memory, that is, erase and write operations. According to our simulation results, the proposed buffer system can reduce the write and erase operations by about 73% and 79% for spec application respectively, compared with a fully associative buffer with two times more space. Futhermore, the average memory access time can improve by about 60% compared with other large buffer systems.

Scheduling plays a key role in MapReduce systems. In this paper, we explore the efficiency of an MapReduce cluster running lots of independent and continuously arriving MapReduce jobs. Data locality and load balancing are two important factors to improve computation efficiency in MapReduce systems for data-intensive computations. Traditional cluster scheduling technologies are not well suitable for MapReduce environment, there are some in-used schedulers for the popular open-source Hadoop MapReduce implementation, however, they can not well optimize both factors. Our main objective is to minimize total flowtime of all jobs, given it's a strong NP-hard problem, we adopt some effective heuristics to seek satisfied solution. In this paper, we formalize the scheduling problem as job selection problem, a load balance aware job selection algorithm is proposed, in task level we design a strict data locality tasks scheduling algorithm for map tasks on map machines and a load balance aware scheduling algorithm for reduce tasks on reduce machines. Comprehensive experiments have been conducted to compare our scheduling strategy with well-known Hadoop scheduling strategies. The experimental results validate the efficiency of our proposed scheduling strategy.

Text sentiment classification aims to automatically classify subjective documents into different sentiment-oriented categories (e.g. positive/negative). Given the high dimensionality of features describing documents, how to effectively select the most useful ones, referred to as sentiment-bearing features, with a lack of sentiment class labels is crucial for improving the classification performance. This paper proposes an unsupervised sentiment-bearing feature selection method (USFS), which incorporates sentiment discriminant analysis (SDA) into sentiment strength calculation (SSC). SDA applies traditional linear discriminant analysis (LDA) in an unsupervised manner without losing local sentiment information between documents. We use SSC to calculate the overall sentiment strength for each single feature based on its affinities with some sentiment priors. Experiments, performed using benchmark movie reviews, demonstrated the superior performance of USFS.

Traffic classification has recently gained much attention in both academic and industrial research communities. Many machine learning methods have been proposed to tackle this problem and have shown good results. However, when applied to traffic with out-of-sequence packets, the accuracy of existing machine learning approaches decreases dramatically. We observe the main reason is that the out-of-sequence packets change the spatial representation of feature vectors, which means the property of linear mapping relation among features used in machine learning approaches cannot hold any more. To address this problem, this paper proposes an Improved Dynamic Time Warping (IDTW) method, which can align two feature vectors using non-linear alignment. Experimental results on two real traces show that IDTW achieves better classification accuracy in out-of-sequence traffic classification, in comparison to existing machine learning approaches.

In this letter, we investigate the performance of cooperative decode-and-forward multiple-input multiple-output relaying system using orthogonal space-time block codes with piecewise-linear (PL) receiver over correlated Nakagami-m fading channels for integer values of m. We derive the closed-form expression for the exact bit error rates of binary phase shift keying signals. The analytical expression is validated through numerical results. It is shown that the performance of PL receiver outperforms that of conventional maximal ratio combining receiver.

Conventional CPM signals employ information sequence with time-unlimited phase shaping pulse (PSP) to achieve power and bandwidth efficient transmission. On the contrary, information sequence using time-limited PSP was believed to produce power-wasting data-independent discrete spectral lines in CPM spectra, and was suggested to be avoided. In this paper, we revisit this problem and adopt the time-limited PSP to replace the one with time-unlimited, it turns out to have an alternative solution to the CPM scheme. We first modify the spectral computing formula for the CPM with time-limited PSP (or CPM-TL) from conventional CPM formula and show that the discrete spectral lines appeared in the power density spectrum of CPM-TL signals can be diminished or become negligible by appropriately choosing PSP. We also show that this class of CPM can use any real number modulation index (h) and the resultant trellis structure of CPM guarantees the maximum constraint length allowed by the number of states in the MLSD receiver. Finally, the energy-bandwidth performance of CPM using time-limited PSP is investigated and compared with conventional CPM with time-unlimited PSP. From numerical results we show that, under the same number of states in the MLSD receiver and bandwidth occupancy, this subclass of CPM could outperform the conventional CPM up to 6dB coding gain, for h<1, in many cases.

In this paper, penetration phenomenon of an early-time (E1) high altitude electromagnetic pulse (HEMP) into dispersive underground multilayer structures is analyzed using electromagnetic modeling of wave propagation in frequency dependent lossy media. The electromagnetic pulse is dealt with in the power spectrum ranging from 100kHz to the 100MHz band, considering the fact that the power spectrum of the E1 HEMP rapidly decreases 30dB below its maximum value beyond the 100MHz band. In addition, the propagation channel consisting of several dielectric materials is modeled with the dispersive relative permittivity of each medium. Based on source and channel models, the propagation phenomenon is analyzed in the frequency and time domains. The attenuation levels at a 100m underground point are observed to be about 15 and 20dB at 100kHz and 1MHz, respectively, and the peak level of the penetrating electric field is found 5.6kV/m. To ensure the causality of the result, we utilize the Hilbert transform.

We consider uplink multi-carrier code-division multiple access (MC-CDMA) systems in a multi-cell environment. It is assumed that all intra-cell users employ Alamouti's simple space-time block coding (STBC), which is known to the base station receiver, but the receiver has no information on whether inter-cell users employ STBC or not. In this case we propose a blind adaptive minimum output energy (MOE) receiver for uplink STBC MC-CDMA, which is designed to perfectly remove the interference from intra-cell users by using the spreading sequence information on all intra-cell users and to reduce the interference from inter-cell users by minimizing the constrained output energy. Analysis and simulation results show that the proposed adaptive receiver has a faster convergence rate and higher steady-state signal-to-interference plus noise ratio (SINR) than a conventional scheme in which only the spreading code information of the desired user is utilized.

We evaluated the behavior of a multi-user multiple-input multiple-output (MIMO) system in time-varying channels using measured data. A base station for downlink or broadcast transmission requires downlink channel state information (CSI), which is outdated in time-varying environments and we encounter degraded performance due to interference. One of the countermeasures against time-variant environments is predicting channels with an autoregressive (AR) model-based method. We modified the AR prediction for a time division duplex system. We conducted measurement campaigns in indoor environments to verify the performance of the scheme of channel prediction in an actual environment and measured channel data. We obtained the bit-error rate (BER) using these data. The AR-model-based technique of prediction assuming the Jakes' model was found to reduce BER. Also, the optimum AR-model order was investigated by using the channel data we measured.

Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. In this paper we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.

MIMO transmission technologies have become an essential component of cellular systems such as Long Term Evolution (LTE) and LTE-Advanced. Recently, evaluating the communication performance of mobile users in cellular MIMO systems has become an urgent requirement. In this paper, we propose dynamic MIMO channel modeling for the urban environment. Our proposal is based on Geometry-based Stochastic Channel Modeling (GSCM). The cluster parameters such as the local scatterer locations around the measurement course are estimated by applying the particle filtering to measured data. We carried out radio propagation measurements in an urban environment at 3.35GHz band, and generated the dynamic channel from the measured data. The experiments showed that both the spreads and auto-correlation of Time of Arrival (ToA), Angle of Arrival (AoA) and Angle of Departure (AoD) were reconstructed within the acceptable error range in our dynamic channel model.

This paper proposes an opportunistic feedback and user selection method for a multiuser two-way relay channel (MU-TWRC) in a time-varying environments where a base station (BS) and a selected mobile station (MS), one of K moving MSs, exchange messages during two time slots via an amplify-and-forward relay station. Specifically, under the assumption of perfect channel reciprocity, we analyze the outage probabilities of several channel feedback scenarios, including the proposed scheme. Based on the analysis, the transmission rates are optimized and the optimal user selection method is proposed to maximize the expected sum throughput. The simulation results indicate that, with opportunistic feedback, the performance can be significantly improved compared to that without feedback. Moreover, the performance is nearly identical to that with full feedback, and close to the case of perfect channel state information at BS for low mobility MSs.

Intruder detection method by utilizing a time variation of Multiple Input Multiple Output (MIMO) channel (MIMO Sensor) has been proposed. Although the channel capacity on the MIMO transmission is severely degraded in time variant channels, we can take advantage of this feature in MIMO Sensor applications. We have already demonstrated the effectiveness of 2×2 MIMO sensor using 2.4GHz band at a small room (Size is 50m2). In this paper, we compare the detection probability of SIMO/MIMO sensors when the number of channel responses are same between SIMO/MIMO sensors: The numbers of transmit and receive antennas are 1 and 4 (SIMO), it is clarified that 2 and 2 (MIMO). The measurement was carried out at the room with the size of 140m2. From the measured results, 2×2 MIMO sensor obtains the same or higher detection probability compared to 4×1 SIMO sensor regardless of the measured location.

This paper proposes a coarse-fine Time-to-Digital Converter (TDC), based on a Ring-Tapped Delay Line (RTDL). The TDC achieves the picosecond's level timing resolution and microsecond's level dynamic range at low cost. The TDC is composed of two coarse time measurement blocks, a time residue generator, and a fine time measurement block. In the coarse blocks, RTDL is constructed by redesigning the conventional Tapped Delay Line (TDL) in a ring structure. A 12-bit counter is employed in one of the two coarse blocks to count the cycle times of the signal traveling in the RTDL. In this way, the input range is increased up to 20.3µs without use of an external reference clock. Besides, the setup time of soft-edged D-flip-flops (SDFFs) adopted in RTDL is set to zero. The adjustable time residue generator picks up the time residue of the coarse block and propagates the residue to the fine block. In the fine block, we use a Vernier Ring Oscillator (VRO) with MOS capacitors to achieve a scalable timing resolution of 11.8ps (1 LSB). Experimental results show that the measured characteristic curve has high-level linearity; the measured DNL and INL are within ± 0.6 LSB and ± 1.5 LSB, respectively. When stimulated by constant interval input, the standard deviation of the system is below 0.35 LSB. The dead time of the proposed TDC is less than 650ps. When operating at 5 MSPS at 3.3V power supply, the power consumption of the chip is 21.5mW. Owing to the use of RTDL and VRO structures, the chip core area is only 0.35mm × 0.28mm in a 0.35µm CMOS process.